File:Erdős–Rényi model random graphs.pdf

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English: Erdős–Rényi model random graphs with different values of P. The plot has been obtained by the following Python code:

import networkx as nx import matplotlib.pyplot as plt from scipy.stats import bernoulli colors = ["blue", "red", "green", "yellow"] probs = [0.1, 0.3, 0.5, 0.8] N=20 plt.figure() for n in range(len(colors)): 

   p=probs[n]
   G = nx.Graph()
   G.add_nodes_from(range(N))
   for node1 in G.nodes():
       for node2 in G.nodes():
           if bernoulli.rvs(p=p) and node1>node2:
               G.add_edge(node1, node2)
   plt.subplot(2,2,n+1)
   nx.draw(G, node_color=colors[n], edge_color='gray', label="P=" + str(probs[n]), node_size=12)
   plt.legend(fontsize = 'small', labelspacing =-1)

plt.show()

plt.savefig("fig.eps")
Català: Grafs aleatoris obtinguts a partir del model d'Erdős-Rényi amb diferents valors del paràmetre P. La gràfica s'ha obtingut a partir del codi de Python: 
import networkx as nx

import matplotlib.pyplot as plt from scipy.stats import bernoulli colors = ["blue", "red", "green", "yellow"] probs = [0.1, 0.3, 0.5, 0.8] N=20 plt.figure() for n in range(len(colors)): 

   p=probs[n]
   G = nx.Graph()
   G.add_nodes_from(range(N))
   for node1 in G.nodes():
       for node2 in G.nodes():
           if bernoulli.rvs(p=p) and node1>node2:
               G.add_edge(node1, node2)
   plt.subplot(2,2,n+1)
   nx.draw(G, node_color=colors[n], edge_color='gray', label="P=" + str(probs[n]), node_size=12)
   plt.legend(fontsize = 'small', labelspacing =-1)

plt.show()

plt.savefig("fig.eps")
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Source Own work
Author Jordiventura96

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